license: apache-2.0
language: en
pipeline_tag: text-classification
tags:
- deberta-v2
- deberta-v3
- sentiment-analysis
- federal-reserve
- economics
- finance
- central-bank-communication
- fomc
widget:
- text: >-
The Committee judges that the risks to the outlook for economic activity
are weighted to the downside.
example_title: Negative example (pessimistic economic assessment)
- text: >-
Economic activity has continued to expand at a solid pace, and labor
market conditions remain strong.
example_title: Positive example (optimistic economic assessment)
FedDeBERTa
FedDeBERTa is a fine-tuned microsoft/deberta-v3-base model for binary sentiment classification of Federal Reserve communications (FOMC statements, minutes, and related monetary-policy text). It classifies a sentence's economic-assessment tone as Positive (optimistic — language suggesting economic strength, growth, or confidence in the outlook) or Negative (pessimistic — language suggesting economic weakness, decline, or concern about conditions).
Sentiment is not the same task as monetary-policy stance. Stance classification asks whether policy is easing or tightening (dovish vs. hawkish); sentiment, as used here, asks only whether the text characterizes economic conditions positively or negatively. The two are related — markets sometimes read expressed economic concern as a signal of anticipated easing, and expressed optimism as a signal of anticipated tightening — but sentiment is not a policy vote and is not a substitute for stance. This model was trained and evaluated on sentiment labels only; it does not predict monetary-policy stance.
This is the BASE variant, fine-tuned directly from the general-purpose deberta-v3-base checkpoint (no domain-adaptive pretraining). A companion model, FedDeBERTa-DAPT, applies continued domain-adaptive pretraining on Federal Reserve text before fine-tuning on the same task.
Both models were developed as part of the dissertation "Domain Adaptive Pretraining for Federal Reserve Sentiment Analysis: A Systematic Study of Small-Corpus Adaptation, Knowledge Distillation, and Cross-Bank Transfer" by Christopher S. Bennett, University of Arkansas at Little Rock.
⚠️ Disclaimer
This is an academic research artifact released alongside a dissertation. It is not intended as financial or investment advice, and outputs should not be used as the sole basis for trading, investment, or policy decisions. Performance figures below reflect a held-out academic test set and may not generalize to other time periods, institutions, or communication styles. Use in any production or decision-making context is at the deployer's own risk.
Model details
| Base architecture | DebertaV2ForSequenceClassification (DeBERTa-v3-base backbone) |
| Hidden size | 768 |
| Layers / attention heads | 12 / 12 |
| Tokenizer | SentencePiece (Unigram), 128,001 vocabulary entries |
| Labels | 0: Negative, 1: Positive |
| Dropout (attention / hidden) | 0.05 / 0.05 |
| License | Apache-2.0 |
A note on vocab_size: this model's config.json reports vocab_size: 128100, and its embedding matrix has shape (128100, 768) — 99 rows larger than the tokenizer's actual 128,001-entry vocabulary. This is an inherited quirk from the upstream microsoft/deberta-v3-base checkpoint (99 reserved/unused embedding rows that no token id can ever reach) rather than a bug specific to this fine-tune; it was verified empirically before release and has no effect on model behavior. See REPRODUCIBILITY.md in the companion GitHub repo for details.
Training data
Fine-tuned on a labeled corpus of Federal Reserve communication sentences (source file referenced internally as FED_prelabelled_sent_fixed.csv), with each sentence labeled Positive or Negative for economic-assessment tone. Full dataset construction and labeling methodology are described in the dissertation.
Evaluation
Evaluated on a frozen, grouped stratified 80/20 held-out test split (seed=42, N=1,322: 718 Negative / 604 Positive), verified independently against archived model predictions and cross-checked row-by-row against the frozen split manifest (100% match).
| Metric | Value |
|---|---|
| Weighted F1 | 81.19% |
| Accuracy | 81.24% |
| Negative — precision / recall / F1 | 0.8133 / 0.8496 / 0.8311 |
| Positive — precision / recall / F1 | 0.8112 / 0.7682 / 0.7891 |
Compared to the DAPT variant, this BASE model performs within statistical noise of the DAPT model on this test set (ΔF1-weighted = +0.16pp favoring DAPT, 95% CI [-1.44, +1.76]pp, Holm-corrected p = 1.000 — not statistically significant). See the dissertation and REPRODUCIBILITY.md for the full statistical methodology (McNemar's test, Holm-Bonferroni correction, bootstrap CIs).
Usage
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
tokenizer = AutoTokenizer.from_pretrained("cbenne23/FedDeBERTa")
model = AutoModelForSequenceClassification.from_pretrained("cbenne23/FedDeBERTa")
model.eval()
text = "The Committee judges that the risks to the outlook for economic activity are weighted to the downside."
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
with torch.no_grad():
logits = model(**inputs).logits
pred_id = torch.argmax(logits, dim=-1).item()
print(model.config.id2label[pred_id]) # "Negative"
Checkpoint integrity
model.safetensors SHA-256: aef5deed2088793e9a416ca7ea7c89ee9d1bd01d9c64886f41a9e82c7af6a188
Citation
If you use this model, please cite the dissertation:
@phdthesis{bennett_fed_sentiment,
author = {Bennett, Christopher S.},
title = {Domain Adaptive Pretraining for Federal Reserve Sentiment Analysis: A Systematic Study of Small-Corpus Adaptation, Knowledge Distillation, and Cross-Bank Transfer},
school = {University of Arkansas at Little Rock},
year = {2026}
}